140.778.01
Advanced Statistical Computing
 Location:
 East Baltimore
 Term:
 2nd term
 Department:
 Biostatistics
 Credits:
 3 credits
 Academic Year:
 2022  2023
 Instruction Method:
 TBD
 Class Times:

 M W, 10:30  11:50am
 Auditors Allowed:
 Yes, with instructor consent
 Grading Restriction:
 Letter Grade or Pass/Fail
 Course Instructor:
 Contact:
 Kasper Hansen
 Frequency Schedule:
 Every Other Year
 Next Offered:
 2024  2025
 Resources:
 Prerequisite:
Prior programming experience; at least one year of doctorallevel statistics/biostatistics theory and methods courses; 140.776
 Description:

Covers the theory and application of common algorithms used in statistical computing. Topics include root finding, optimization, numerical integration, Monte Carlo, Markov chain Monte Carlo, stochastic optimization and bootstrapping. Some specific algorithms discussed include: NewtonRaphson, EM, MetropolisHastings algorithm, Gibbs sampling, simulated annealing, Gaussian quadrature, Romberg integration, etc. Also discusses applications of these algorithms to real research problems.
 Learning Objectives:

Upon successfully completing this course, students will be able to:
 Describe common deterministic statistical algorithms, such as root finding, numerical integration methods, NewtonRaphson, quasiNewton methods, EM
 Describe common stochastic algorithms used in statistics, such as Monte Carlo methods, Markov Chain Monte Carlo, stochastic optimization, Gibbs sampling, MetropolisHastings method
 Understand mathematical properties of common statistical algorithms
 Implement statistical algorithms using a highlevel statistical programming language
 Methods of Assessment:
Method of student evaluation based on computing and theoretical assignments
 Instructor Consent:
No consent required